---
license: mit
language:
- en
pipeline_tag: text-generation
metrics:
- accuracy
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This produces novel color names and hexadecimal values. It was fine tuned using https://www.kaggle.com/datasets/avi1023/color-names

## Model Details

The model is great for beginners learning PyTorch, fine tuning, and training a simple model.

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Seth Hammock
- **Funded by [optional]:** Seth Hammock
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Transformer
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** GPT2

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This is a model was created as an exercise in autoregressive language models. Use it as a beginner and train it on a larger datasert to improve its output. The idea is that 
you can train it easily using free resources on Google Colab, or train it on a laptop.

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Evaluating the model without additional tuning will produce color names with color codes, RGB values, hue degrees, HSL and HSV. 

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

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## Bias, Risks, and Limitations

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The color names don't always align with the colors and at times will produce improperly formed hexadecimal values. 

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]


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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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